Kiseleva Olga I, Arzumanian Viktoriia A, Poverennaya Ekaterina V, Pyatnitskiy Mikhail A, Ilgisonis Ekaterina V, Zgoda Victor G, Plotnikova Oksana A, Sharafetdinov Khaider K, Lisitsa Andrey V, Tutelyan Victor A, Nikityuk Dmitry B, Archakov Alexander I, Ponomarenko Elena A
Institute of Biomedical Chemistry, Pogodinskaya Street 10/8, 119121 Moscow, Russia.
Federal Research Centre of Nutrition, Biotechnology and Food Safety, Russian Academy of Sciences, Ustinsky Passage 2/14, 109240 Moscow, Russia.
J Pers Med. 2021 Jan 21;11(2):64. doi: 10.3390/jpm11020064.
Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of "cheap calories" are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy.
肥胖是一种可怕的慢性疾病，自1975年以来其发病率已增长了两倍。预计它不会放缓，仍将是可预防死亡的主要原因之一，并导致临床和经济负担加重。不良的生活方式选择以及过量摄入“廉价热量”是肥胖的主要成因，会引发2型糖尿病、心血管疾病及其他合并症。了解导致肥胖发生的分子机制至关重要，因为这可能会催生抗肥胖靶点和早期肥胖生物标志物，从而有助于区分代谢综合征。这种疾病的复杂性，再加上代谢健康型肥胖现象，促使我们开展以数据为中心、产生假设的初步研究，旨在找出104名体重正常者和肥胖者的经典临床血液检测参数与蛋白质组图谱之间的相关性。结果，我们整理出了蛋白质模式，根据这些蛋白质的有无能够较好地预测患者的体重。我们认为，这种具有高预测能力的蛋白质组模式应有助于将潜在候选物转化为临床可用的生物标志物，用于肥胖治疗的早期分层。
